TY - GEN
T1 - A real-time specific weed recognition system by measuring weeds density through mask operation
AU - Ahmed, Imran
AU - Ahmad, Zaheer
AU - Islam, Muhammad
AU - Adnan, Awais
PY - 2008
Y1 - 2008
N2 - The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm which is based on Measuring Weeds Density through Mask operation is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 95 % classification accuracy over 170 sample images (broad and narrow) with 70 samples from each category of weeds.
AB - The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm which is based on Measuring Weeds Density through Mask operation is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 95 % classification accuracy over 170 sample images (broad and narrow) with 70 samples from each category of weeds.
KW - Image processing
KW - Mask
KW - Real-time recognition
KW - Weed density
KW - Weed detection
UR - http://www.scopus.com/inward/record.url?scp=84879659289&partnerID=8YFLogxK
U2 - 10.1007/978-1-4020-8735-6_42
DO - 10.1007/978-1-4020-8735-6_42
M3 - Conference contribution
AN - SCOPUS:84879659289
SN - 9781402087349
T3 - Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering
SP - 221
EP - 225
BT - Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering
T2 - 2007 International Conference on Systems, Computing Sciences and Software Engineering, SCSS 2007, Part of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, CISSE 2007
Y2 - 3 December 2007 through 12 December 2007
ER -